Field of the Invention
This invention relates to apparatus and method incorporating a neural network for retrieving signals embedded in noise and analyzing the retrieved signals. In a first stage, the noise is filtered out of the input signal to retrieve a filtered data signal. In a second stage, the filtered data signals are analyzed to determine their behavior. In a third stage, the significance of the behavior of the signals is determined. The apparatus and method of the invention are usable in a variety of applications where signals are embedded in noise, including, without limitation, earthquake and other seismic data, stock market data ( including stocks, options, futures, commodities, currencies, indices, foreign government and municipal securities, bonds, treasury bills, foreign exchange rates, interest rates), high definition television signals, radar, sonar, ultrasound imaging, edge detection, detection of presence of certain elements, such as lead, from X-rays of the bone, and the like.
The present invention is preferably implemented using a computer and a digitized data processor. In a preferred embodiment of the present invention, the computer receives stock market data input signals, learns the pattern of signals to predict future behavior, identifies deviations from the learned patterns, analyzes the deviations in view of previously stored deyiation patterns of stock market behavior, and ultimately identifies the meaning of the deviations, such as profitable buy/sell selections in the stock market.
The present invention includes a filter to filter out noise and focus on what is essential in the market. The filter separates from the chaos of the market those activities which indicate a trend. The present invention further includes a neural network that has learned and will continue to learn stock market data behavior. The neural network applies learned patterns to incoming data to identify and recommend possible highly profitable buy/sell selections that may or may not follow the learned patterns. The present invention includes an expert system which prioritizes the recommended buy/sell selections.
It is useful to analyze signals embedded in noise, such as stock, using a neural network for the following reasons: (1) unlike traditional expert systems where knowledge is represented explicitly in the form of rules, neural networks can learn from examples; (2) neural networks have the ability to recognize patterns in data and classify incoming data into previously learned examples or, trained pattern sets; and (3) neural networks are known for their ability to learn from experience, to generalize, and to recognize and predict patterns.
The neural network portion of the apparatus and method of the present invention includes a self adaptive and variant error ("SAVE") filter. The SAVE filter views the incoming data prior to the onset of a signal of potential interest from a simple quadratic error filter. Once the signal of potential interest impinges the system of the present invention, as identified by the error:
1. the coefficients related to the data prior to the signal are "frozen";
2. the filter becomes an adaptive autoregressive moving average ("ARMA") filter; and
3. the usual nonlinearities of an adaptive ARMA are avoided, since the "frozen" coefficients retain the mathematics within the quadratic regime.
The SAVE filter provides determination of:
1. the optimum approach to freezing the coefficients;
2. the optimum filter lengths for each portion of the filter;
3. the optimum adaptation time associated with each portion of the SAVE filter; and
4. improved signal detection, identification, and pattern recognition after determining (1) through (3).